A founder demonstrated how an AI agent can watch live user sessions, analyze conversion behavior, and then autonomously create and deploy A/B tests for an app's paywall. This compresses a process that previously took months of manual work by a growth team into a single night with one prompt.

To scale a testing program effectively, empower distributed marketing teams to run their own experiments. Providing easy-to-use tools within a familiar platform (like Sitecore XM Cloud) democratizes the process, leveraging local and industry-specific knowledge while avoiding the bottleneck of a central CRO team.

The traditional "test and learn" mantra is flawed because teams often start with a weak set of creative variants. By using predictive AI to generate a diverse but pre-vetted, high-performance set of options, marketers can ensure their tests are more meaningful and aren't just optimizing a bad strategy.

The true power of AI agents lies in full-cycle automation. An agent can be built to scrape customer pain points for ad ideas, generate creative, publish campaigns via API, analyze live performance data, and then automatically reallocate budget by disabling underperformers and scaling winners.

Brunson reverse-engineers competitors' successful marketing campaigns by transcribing every session, email, and text. He then uses AI to analyze this massive dataset, identify best practices they are using that he isn't, and incorporates those learnings into his own launches for dramatically better results.

AI agents can continuously experiment with variables like subject lines, send times, and offers for each individual user. This level of granular, ongoing A/B testing is impossible to manage manually, unlocking significant performance lifts that compound over time.

The common view of AI is to increase efficiency or replace headcount. A more powerful approach is to maintain your team and leverage AI for abundance. Use it to triple your output, running five marketing campaigns instead of one and exploring numerous variations to dramatically increase growth.

Package pre-configured Autoresearch loops to solve a single, painful problem for a specific niche, like an Amazon listing optimizer or an email tuner for realtors. Sell it as a simple, automated monthly subscription service.

The true power of AI agents lies in creating a recursive feedback loop. By ingesting ad performance data, they can autonomously analyze what works, iterate on creative, and launch new versions, far outpacing human-led optimization cycles.